{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "db522394",
   "metadata": {},
   "source": [
    "### scikit-learn\n",
    "\n",
    "Scikit-learn（以前称为scikits.learn，也称为sklearn）是针对Python 编程语言的免费软件机器学习库 [1]  。它具有各种分类，回归和聚类算法，包括支持向量机，随机森林，梯度提升，k均值和DBSCAN，并且旨在与Python数值科学库NumPy和SciPy联合使用。\n",
    "\n",
    "官网：https://scikit-learn.org/stable/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d97af670",
   "metadata": {},
   "source": [
    "```bash\n",
    "pip install scikit-learn\n",
    "```\n",
    "\n",
    "```bash\n",
    "python -m pip install scikit-learn\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d358121",
   "metadata": {},
   "source": [
    "## 线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "694f0468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   Unnamed: 0      country iso_code        date  total_vaccinations  \\\n0           0  Afghanistan      AFG  2021-05-11            504502.0   \n1           1  Afghanistan      AFG  2021-05-20            547901.0   \n2           2  Afghanistan      AFG  2021-05-24            573277.0   \n3           3  Afghanistan      AFG  2021-05-26            590454.0   \n4           4  Afghanistan      AFG  2021-05-27            593313.0   \n\n   people_vaccinated  people_fully_vaccinated  New_deaths  population  \\\n0           448878.0                  55624.0          12  40094444.0   \n1           470341.0                  77560.0          10  40094444.0   \n2           476367.0                  96910.0          10  40094444.0   \n3           479372.0                 111082.0          19  40094444.0   \n4           479574.0                 113739.0          14  40094444.0   \n\n      ratio  \n0  1.119552  \n1  1.173083  \n2  1.188112  \n3  1.195607  \n4  1.196111  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>country</th>\n      <th>iso_code</th>\n      <th>date</th>\n      <th>total_vaccinations</th>\n      <th>people_vaccinated</th>\n      <th>people_fully_vaccinated</th>\n      <th>New_deaths</th>\n      <th>population</th>\n      <th>ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>Afghanistan</td>\n      <td>AFG</td>\n      <td>2021-05-11</td>\n      <td>504502.0</td>\n      <td>448878.0</td>\n      <td>55624.0</td>\n      <td>12</td>\n      <td>40094444.0</td>\n      <td>1.119552</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>Afghanistan</td>\n      <td>AFG</td>\n      <td>2021-05-20</td>\n      <td>547901.0</td>\n      <td>470341.0</td>\n      <td>77560.0</td>\n      <td>10</td>\n      <td>40094444.0</td>\n      <td>1.173083</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>Afghanistan</td>\n      <td>AFG</td>\n      <td>2021-05-24</td>\n      <td>573277.0</td>\n      <td>476367.0</td>\n      <td>96910.0</td>\n      <td>10</td>\n      <td>40094444.0</td>\n      <td>1.188112</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>Afghanistan</td>\n      <td>AFG</td>\n      <td>2021-05-26</td>\n      <td>590454.0</td>\n      <td>479372.0</td>\n      <td>111082.0</td>\n      <td>19</td>\n      <td>40094444.0</td>\n      <td>1.195607</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>Afghanistan</td>\n      <td>AFG</td>\n      <td>2021-05-27</td>\n      <td>593313.0</td>\n      <td>479574.0</td>\n      <td>113739.0</td>\n      <td>14</td>\n      <td>40094444.0</td>\n      <td>1.196111</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('covid_vaccination_vs_death_ratio.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db9d8a95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[-0.46253682]]\n",
      "Intercept:  [8.90583365e-17]\n",
      "The prediction of New Deaths is :\n",
      " [[1786.43755105]\n",
      " [ 797.74419925]\n",
      " [ 672.2953259 ]\n",
      " [ 787.54672956]\n",
      " [ 707.04351546]\n",
      " [ 594.20544557]\n",
      " [ 812.00927589]\n",
      " [ 892.76273539]\n",
      " [1924.15686139]\n",
      " [ 804.84418051]\n",
      " [1830.28848304]\n",
      " [ 699.87543115]\n",
      " [1331.69082373]\n",
      " [ 729.91362302]\n",
      " [ 774.27180134]\n",
      " [ 721.93746503]\n",
      " [ 623.4098197 ]\n",
      " [1941.1430102 ]\n",
      " [ 679.28795577]\n",
      " [1064.36668375]\n",
      " [1017.44110635]\n",
      " [ 991.46784356]\n",
      " [1863.24713328]\n",
      " [ 738.58718978]\n",
      " [ 800.69482422]\n",
      " [1683.11182917]\n",
      " [1354.01957595]\n",
      " [1693.33659824]\n",
      " [ 857.5213042 ]\n",
      " [1882.7654361 ]\n",
      " [ 636.2182985 ]\n",
      " [ 654.95991144]\n",
      " [ 886.2161193 ]\n",
      " [1769.25060272]\n",
      " [ 756.22926069]\n",
      " [1086.36744449]\n",
      " [ 703.430835  ]\n",
      " [1918.84350489]\n",
      " [1021.07919275]\n",
      " [1121.18541414]\n",
      " [1157.55119144]\n",
      " [ 866.61535708]\n",
      " [1823.92800581]\n",
      " [ 849.14452094]\n",
      " [1870.03850379]\n",
      " [ 633.92410957]\n",
      " [ 900.28826772]\n",
      " [1438.58711051]\n",
      " [1112.16616575]\n",
      " [ 823.17564011]\n",
      " [1171.20729657]\n",
      " [ 726.01902934]\n",
      " [1536.48933843]\n",
      " [ 615.28435029]\n",
      " [1879.94094142]\n",
      " [1586.84036193]\n",
      " [ 954.39556221]\n",
      " [1385.45184149]\n",
      " [ 662.58603288]\n",
      " [1314.91826869]\n",
      " [ 795.41388616]\n",
      " [1747.17308996]\n",
      " [1468.0984792 ]\n",
      " [ 897.44238391]\n",
      " [1453.11592332]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_usa = df[df['country']=='United States of America']\n",
    "\n",
    "# define x,y\n",
    "x = df_usa[['ratio']] # 比率\n",
    "y = df_usa[['New_deaths']]\n",
    "\n",
    "# split - 随机切分\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25,random_state=42)\n",
    "\n",
    "# Standard Normalization(x)\n",
    "\"\"\"\n",
    "x* = (x - μ)/σ\n",
    "其中μ为所有样本数据的均值，σ为所有样本数据的标准差。\n",
    "\"\"\"\n",
    "std_x = StandardScaler()\n",
    "x_train = std_x.fit_transform(x_train)\n",
    "x_test = std_x.fit_transform(x_test)\n",
    "\n",
    "# Standard Normalization(y)\n",
    "std_y = StandardScaler() # 标准化\n",
    "y_train = std_y.fit_transform(y_train)\n",
    "y_test = std_y.fit_transform(y_test)\n",
    "\n",
    "# fitting in\n",
    "liner = LinearRegression()\n",
    "liner.fit(x_train, y_train) # 模型的训练 \n",
    "\n",
    "# coefficients\n",
    "print ('Coefficients: ', liner.coef_) # 斜率 k\n",
    "print ('Intercept: ',liner.intercept_) # 截距 b  y = kx + b \n",
    "\n",
    "# prediction\n",
    "y_pre = std_y.inverse_transform(liner.predict(x_test))# inverse to original value\n",
    "print('The prediction of New Deaths is :\\n',y_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3546ba83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Liner Regression')"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "x = df_usa['ratio']\n",
    "y = df_usa['New_deaths']\n",
    "\n",
    "plt.figure(figsize=(8,6),dpi=100)\n",
    "sns.regplot(x=x, y=y)\n",
    "plt.xlabel('Ratio(USA)',fontsize=12)\n",
    "plt.ylabel('New Deaths',fontsize=12)\n",
    "plt.title('Liner Regression',fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "deeae717",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Mean Squared Error(MSE) of y_pre is : 660882.90377\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(std_y.inverse_transform(y_test),y_pre)\n",
    "\n",
    "# make sure the output not show with scientific notation\n",
    "def as_float(x):\n",
    "    y='{:.5f}'.format(x)\n",
    "    return  y\n",
    "\n",
    "print('The Mean Squared Error(MSE) of y_pre is :',as_float(mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c61da12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Mean Absolute Error(MAE) of y_pre is : 603.07769\n"
     ]
    }
   ],
   "source": [
    "mae = mean_absolute_error(std_y.inverse_transform(y_test),y_pre)\n",
    "print('The Mean Absolute Error(MAE) of y_pre is :',as_float(mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8734ccce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测值为:\n",
      " [28.22944896 31.5122308  21.11612841 32.6663189  20.0023467  19.07315705\n",
      " 21.09772798 19.61400153 19.61907059 32.87611987 20.97911561 27.52898011\n",
      " 15.54701758 19.78630176 36.88641203 18.81202132  9.35912225 18.49452615\n",
      " 30.66499315 24.30184448 19.08220837 34.11391208 29.81386585 17.51775647\n",
      " 34.91026707 26.54967053 34.71035391 27.4268996  19.09095832 14.92742976\n",
      " 30.86877936 15.88271775 37.17548808  7.72101675 16.24074861 17.19211608\n",
      "  7.42140081 20.0098852  40.58481466 28.93190595 25.25404307 17.74970308\n",
      " 38.76446932  6.87996052 21.80450956 25.29110265 20.427491   20.4698034\n",
      " 17.25330064 26.12442519  8.48268143 27.50871869 30.58284841 16.56039764\n",
      "  9.38919181 35.54434377 32.29801978 21.81298945 17.60263689 22.0804256\n",
      " 23.49262401 24.10617033 20.1346492  38.5268066  24.58319594 19.78072415\n",
      " 13.93429891  6.75507808 42.03759064 21.9215625  16.91352899 22.58327744\n",
      " 40.76440704 21.3998946  36.89912238 27.19273661 20.97945544 20.37925063\n",
      " 25.3536439  22.18729123 31.13342301 20.39451125 23.99224334 31.54729547\n",
      " 26.74581308 20.90199941 29.08225233 21.98331503 26.29101202 20.17329401\n",
      " 25.49225305 24.09171045 19.90739221 16.35154974 15.25184758 18.40766132\n",
      " 24.83797801 16.61703662 20.89470344 26.70854061 20.7591883  17.88403312\n",
      " 24.28656105 23.37651493 21.64202047 36.81476219 15.86570054 21.42338732\n",
      " 32.81366203 33.74086414 20.61688336 26.88191023 22.65739323 17.35731771\n",
      " 21.67699248 21.65034728 27.66728556 25.04691687 23.73976625 14.6649641\n",
      " 15.17700342  3.81620663 29.18194848 20.68544417 22.32934783 28.01568563\n",
      " 28.58237108]\n",
      "模型中的系数为:\n",
      " [-0.64817766  1.14673408 -0.05949444  0.74216553 -1.95515269  2.70902585\n",
      " -0.07737374 -3.29889391  2.50267196 -1.85679269 -1.75044624  0.87341624\n",
      " -3.91336869]\n",
      "模型中的偏置为:\n",
      " 22.62137203166228\n",
      "误差为:\n",
      " 20.62751376309539\n"
     ]
    }
   ],
   "source": [
    "# 多维的波士顿房价预测模型\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# 1.获取数据\n",
    "data = load_boston()\n",
    "\n",
    "# 2.数据集划分\n",
    "x_train, x_test, y_train, y_test = train_test_split(data.data, data.target, random_state=22)\n",
    "\n",
    "# 3.特征工程-标准化\n",
    "transfer = StandardScaler()\n",
    "x_train = transfer.fit_transform(x_train)\n",
    "x_test = transfer.transform(x_test)\n",
    "\n",
    "# 4.机器学习-线性回归(正规方程)\n",
    "estimator = LinearRegression()\n",
    "estimator.fit(x_train, y_train)\n",
    "# 5.模型评估\n",
    "# 5.1 获取系数等值\n",
    "y_predict = estimator.predict(x_test)\n",
    "print(\"预测值为:\\n\", y_predict) # 预测值\n",
    "print(\"模型中的系数为:\\n\", estimator.coef_) \n",
    "print(\"模型中的偏置为:\\n\", estimator.intercept_) # 截距\n",
    "\n",
    "# 5.2 评价\n",
    "# 均方误差\n",
    "error = mean_squared_error(y_test, y_predict)\n",
    "print(\"误差为:\\n\", error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1e7655c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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